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"""
@Date: 2021/08/12
@description:
"""
import torch
import torch.nn as nn
import numpy as np
from visualization.grad import get_all
class GradLoss(nn.Module):
def __init__(self):
super().__init__()
self.loss = nn.L1Loss()
self.cos = nn.CosineSimilarity(dim=-1, eps=0)
self.grad_conv = nn.Conv1d(1, 1, kernel_size=3, stride=1, padding=0, bias=False, padding_mode='circular')
self.grad_conv.weight = nn.Parameter(torch.tensor([[[1, 0, -1]]]).float())
self.grad_conv.weight.requires_grad = False
def forward(self, gt, dt):
gt_direction, _, gt_angle_grad = get_all(gt['depth'], self.grad_conv)
dt_direction, _, dt_angle_grad = get_all(dt['depth'], self.grad_conv)
normal_loss = (1 - self.cos(gt_direction, dt_direction)).mean()
grad_loss = self.loss(gt_angle_grad, dt_angle_grad)
return [normal_loss, grad_loss]
if __name__ == '__main__':
from dataset.mp3d_dataset import MP3DDataset
from utils.boundary import depth2boundaries
from utils.conversion import uv2xyz
from visualization.boundary import draw_boundaries
from visualization.floorplan import draw_floorplan
def show_boundary(image, depth, ratio):
boundary_list = depth2boundaries(ratio, depth, step=None)
draw_boundaries(image.transpose(1, 2, 0), boundary_list=boundary_list, show=True)
draw_floorplan(uv2xyz(boundary_list[0])[..., ::2], show=True, center_color=0.8)
mp3d_dataset = MP3DDataset(root_dir='../src/dataset/mp3d', mode='train', patch_num=256)
gt = mp3d_dataset.__getitem__(1)
gt['depth'] = torch.from_numpy(gt['depth'][np.newaxis]) # batch size is 1
dummy_dt = {
'depth': gt['depth'].clone(),
}
# dummy_dt['depth'][..., 20] *= 3 # some different
# show_boundary(gt['image'], gt['depth'][0].numpy(), gt['ratio'])
# show_boundary(gt['image'], dummy_dt['depth'][0].numpy(), gt['ratio'])
grad_loss = GradLoss()
loss = grad_loss(gt, dummy_dt)
print(loss)
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